Using weighted linear spatial decomposition to investigate brain activity through a set of fixed current dipoles

Objectives: We developed a method with the aim of decorrelating scalp EEG based on a set of spatial constraints. Methods: We assume that the scalp EEG can be modelled by a small number of current dipoles of fixed location and orientation, placed at regions of interest. The algorithm is based on weighted linear spatial decomposition in order to obtain a weighted solution to the inverse problem. An EEG data matrix is first weighted in favour of a single dipole in the set. The dipole moment is then calculated from the weighted EEG by the pseudo-inverse method. This is repeated for each dipole. Results: Six seizures were processed from 4 patients using the standard least-squares solution and our weighted version. The average cross-correlation between channels was calculated for each case. The first method resulted in a mean drop in cross-correlation of 16.5% from that of the scalp. Our method resulted in a reduction of 34.5%. Conclusions: Our method gives a more spatially decorrelated signal in regions of interest (although it is not intended as an accurate localization tool). Subsequent analysis is more robust and less likely to be dependent on specific recording montages. This is more than could be obtained using a standard least-squares solution using the same model.